{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0e4e340",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/data/run01/sczc619/LML/MetaTSNE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7411db2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting shortest path computation for YelpChi...\n",
      "Computation completed in 3014.49 seconds\n",
      "Saved shortest distance matrix for YelpChi at data/YelpChi_shortest_distance.pkl\n",
      "Starting shortest path computation for Amazon...\n",
      "Computation completed in 833.59 seconds\n",
      "Saved shortest distance matrix for Amazon at data/Amazon_shortest_distance.pkl\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "from scipy.sparse.csgraph import shortest_path\n",
    "from scipy.io import loadmat\n",
    "import pickle\n",
    "import time\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from scipy.sparse.csgraph import connected_components\n",
    "\n",
    "def compute_shortest_path_matrix(dataset_name):\n",
    "    \"\"\"使用SciPy优化实现的最短路径计算\"\"\"\n",
    "    DATADIR = \"data/\"\n",
    "    mat_path = os.path.join(DATADIR, f\"{dataset_name}.mat\")\n",
    "    mat_data = loadmat(mat_path)\n",
    "\n",
    "    # 获取原始稀疏矩阵并转换为无向图\n",
    "    sparse_matrix = mat_data['homo']\n",
    "    sparse_matrix = sparse_matrix.maximum(sparse_matrix.T)  # 确保矩阵对称\n",
    "\n",
    "    # 转换为CSR格式并移除对角线元素\n",
    "    sparse_matrix = sparse_matrix.tocsr()\n",
    "    sparse_matrix.setdiag(0)  # 移除自环边\n",
    "\n",
    "    # 计算最短路径\n",
    "    print(f\"Starting shortest path computation for {dataset_name}...\")\n",
    "    start_time = time.time()\n",
    "\n",
    "    # 使用Dijkstra算法（自动处理非加权图）\n",
    "    dist_matrix = shortest_path(sparse_matrix,\n",
    "                                directed=False,\n",
    "                                method='D',\n",
    "                                unweighted=True)\n",
    "\n",
    "    print(f\"Computation completed in {time.time() - start_time:.2f} seconds\")\n",
    "\n",
    "    # 处理不可达节点\n",
    "    dist_matrix[np.isinf(dist_matrix)] = -1\n",
    "\n",
    "    # 转换为整数类型\n",
    "    dist_matrix = dist_matrix.astype(np.int32)\n",
    "\n",
    "    output_path = os.path.join(DATADIR, f\"{dataset_name}_shortest_distance.pkl\")\n",
    "    with open(output_path, 'wb') as f:\n",
    "        pickle.dump({\n",
    "            'dist_matrix': dist_matrix,\n",
    "            'sparse_matrix': sparse_matrix,\n",
    "            'node_count': sparse_matrix.shape[0],\n",
    "            'dataset': dataset_name\n",
    "        }, f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "    print(f\"Saved shortest distance matrix for {dataset_name} at {output_path}\")\n",
    "    return dist_matrix\n",
    "\n",
    "\n",
    "def main():\n",
    "    \n",
    "    for dataset in [\"YelpChi\", \"Amazon\"]:\n",
    "        dist_matrix = compute_shortest_path_matrix(dataset)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58e6cf53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas version: 2.0.3\n",
      "/data/home/sczc619/run/LML/anaconda3/envs/tsne/bin/python\n",
      "['/data/home/sczc619/run/LML/anaconda3/envs/tsne/lib/python311.zip', '/data/home/sczc619/run/LML/anaconda3/envs/tsne/lib/python3.11', '/data/home/sczc619/run/LML/anaconda3/envs/tsne/lib/python3.11/lib-dynload', '', '/data/home/sczc619/run/LML/anaconda3/envs/tsne/lib/python3.11/site-packages', '/data/run01/sczc619/LML/MetaTSNE', '/data/run01/sczc619/LML/MetaTSNE']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pandas\n",
    "print(\"pandas version:\", pandas.__version__)\n",
    "print(sys.executable)\n",
    "print(sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d4a9775",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "loading data...\n",
      "training...\n",
      "Epoch: 9, Val AUC: 0.8708, Val AP: 0.6211, Val F1: 0.6828, Val G-mean: 0.8076\n",
      "Epoch: 19, Val AUC: 0.9183, Val AP: 0.8440, Val F1: 0.6060, Val G-mean: 0.7950\n",
      "Epoch: 29, Val AUC: 0.8994, Val AP: 0.5250, Val F1: 0.6365, Val G-mean: 0.8110\n",
      "Epoch: 39, Val AUC: 0.9359, Val AP: 0.8581, Val F1: 0.8708, Val G-mean: 0.8774\n",
      "Epoch: 49, Val AUC: 0.9317, Val AP: 0.8164, Val F1: 0.6608, Val G-mean: 0.8347\n",
      "Epoch: 59, Val AUC: 0.9338, Val AP: 0.8464, Val F1: 0.9023, Val G-mean: 0.8749\n",
      "Epoch: 69, Val AUC: 0.9421, Val AP: 0.8563, Val F1: 0.7326, Val G-mean: 0.8713\n",
      "Epoch: 79, Val AUC: 0.9280, Val AP: 0.8610, Val F1: 0.9062, Val G-mean: 0.8726\n",
      "Epoch: 89, Val AUC: 0.9042, Val AP: 0.5754, Val F1: 0.5051, Val G-mean: 0.7183\n",
      "Epoch: 99, Val AUC: 0.9422, Val AP: 0.8532, Val F1: 0.6635, Val G-mean: 0.8404\n",
      "Epoch: 109, Val AUC: 0.9416, Val AP: 0.8609, Val F1: 0.8445, Val G-mean: 0.8826\n",
      "Epoch: 119, Val AUC: 0.9344, Val AP: 0.8578, Val F1: 0.8658, Val G-mean: 0.8821\n",
      "Epoch: 129, Val AUC: 0.9415, Val AP: 0.8565, Val F1: 0.7549, Val G-mean: 0.8691\n",
      "Epoch: 139, Val AUC: 0.9336, Val AP: 0.7786, Val F1: 0.6705, Val G-mean: 0.8427\n",
      "Epoch: 149, Val AUC: 0.9554, Val AP: 0.8750, Val F1: 0.8820, Val G-mean: 0.8828\n",
      "Epoch: 159, Val AUC: 0.9578, Val AP: 0.8864, Val F1: 0.9069, Val G-mean: 0.8849\n",
      "Epoch: 169, Val AUC: 0.9459, Val AP: 0.8710, Val F1: 0.8410, Val G-mean: 0.8982\n",
      "Epoch: 179, Val AUC: 0.9544, Val AP: 0.8770, Val F1: 0.9053, Val G-mean: 0.8755\n",
      "Epoch: 189, Val AUC: 0.9365, Val AP: 0.8245, Val F1: 0.8481, Val G-mean: 0.8750\n",
      "Epoch: 199, Val AUC: 0.9608, Val AP: 0.8829, Val F1: 0.9079, Val G-mean: 0.8821\n",
      "Epoch: 209, Val AUC: 0.9314, Val AP: 0.7791, Val F1: 0.8472, Val G-mean: 0.8205\n",
      "Epoch: 219, Val AUC: 0.9447, Val AP: 0.8749, Val F1: 0.9068, Val G-mean: 0.8758\n",
      "Epoch: 229, Val AUC: 0.9241, Val AP: 0.7774, Val F1: 0.5121, Val G-mean: 0.7253\n",
      "Epoch: 239, Val AUC: 0.9576, Val AP: 0.8570, Val F1: 0.6501, Val G-mean: 0.8488\n",
      "Epoch: 249, Val AUC: 0.9569, Val AP: 0.8664, Val F1: 0.7654, Val G-mean: 0.8927\n",
      "Epoch: 259, Val AUC: 0.9604, Val AP: 0.8787, Val F1: 0.8112, Val G-mean: 0.9022\n",
      "Epoch: 269, Val AUC: 0.9562, Val AP: 0.8600, Val F1: 0.8157, Val G-mean: 0.8960\n",
      "Epoch: 279, Val AUC: 0.9515, Val AP: 0.8657, Val F1: 0.8298, Val G-mean: 0.9003\n",
      "Epoch: 289, Val AUC: 0.9514, Val AP: 0.8817, Val F1: 0.9084, Val G-mean: 0.8852\n",
      "Epoch: 299, Val AUC: 0.9492, Val AP: 0.8545, Val F1: 0.6363, Val G-mean: 0.8318\n",
      "Epoch: 309, Val AUC: 0.9484, Val AP: 0.8617, Val F1: 0.6714, Val G-mean: 0.8513\n",
      "Epoch: 319, Val AUC: 0.9549, Val AP: 0.8870, Val F1: 0.8829, Val G-mean: 0.9117\n",
      "Epoch: 329, Val AUC: 0.9594, Val AP: 0.8770, Val F1: 0.7291, Val G-mean: 0.8832\n",
      "Epoch: 339, Val AUC: 0.9564, Val AP: 0.8860, Val F1: 0.9088, Val G-mean: 0.8792\n",
      "Epoch: 349, Val AUC: 0.9493, Val AP: 0.8478, Val F1: 0.7398, Val G-mean: 0.8769\n",
      "Epoch: 359, Val AUC: 0.9550, Val AP: 0.8854, Val F1: 0.8883, Val G-mean: 0.9016\n",
      "Epoch: 369, Val AUC: 0.9556, Val AP: 0.8895, Val F1: 0.8869, Val G-mean: 0.9070\n",
      "Epoch: 379, Val AUC: 0.9508, Val AP: 0.8642, Val F1: 0.8007, Val G-mean: 0.8885\n",
      "Epoch: 389, Val AUC: 0.9544, Val AP: 0.8827, Val F1: 0.8934, Val G-mean: 0.8940\n",
      "Epoch: 399, Val AUC: 0.9509, Val AP: 0.8718, Val F1: 0.7939, Val G-mean: 0.8938\n",
      "Epoch: 409, Val AUC: 0.9562, Val AP: 0.8894, Val F1: 0.8564, Val G-mean: 0.9105\n",
      "Epoch: 419, Val AUC: 0.9588, Val AP: 0.8944, Val F1: 0.8876, Val G-mean: 0.9043\n",
      "Epoch: 429, Val AUC: 0.9564, Val AP: 0.8898, Val F1: 0.8638, Val G-mean: 0.9042\n",
      "Epoch: 439, Val AUC: 0.9583, Val AP: 0.8870, Val F1: 0.8446, Val G-mean: 0.9046\n",
      "Epoch: 449, Val AUC: 0.9593, Val AP: 0.8950, Val F1: 0.9110, Val G-mean: 0.9006\n",
      "Epoch: 459, Val AUC: 0.9596, Val AP: 0.8829, Val F1: 0.8510, Val G-mean: 0.9144\n",
      "Epoch: 469, Val AUC: 0.9529, Val AP: 0.8756, Val F1: 0.8238, Val G-mean: 0.9063\n",
      "Epoch: 479, Val AUC: 0.9475, Val AP: 0.8693, Val F1: 0.8747, Val G-mean: 0.8957\n",
      "Epoch: 489, Val AUC: 0.9465, Val AP: 0.8744, Val F1: 0.8866, Val G-mean: 0.8926\n",
      "Epoch: 499, Val AUC: 0.9563, Val AP: 0.8788, Val F1: 0.8551, Val G-mean: 0.9047\n",
      "Epoch: 509, Val AUC: 0.9381, Val AP: 0.8637, Val F1: 0.8234, Val G-mean: 0.9010\n",
      "Epoch: 519, Val AUC: 0.9542, Val AP: 0.8739, Val F1: 0.7791, Val G-mean: 0.8957\n",
      "Epoch: 529, Val AUC: 0.9432, Val AP: 0.8594, Val F1: 0.8371, Val G-mean: 0.8944\n",
      "Epoch: 539, Val AUC: 0.9341, Val AP: 0.8515, Val F1: 0.7869, Val G-mean: 0.8913\n",
      "Epoch: 549, Val AUC: 0.9349, Val AP: 0.8572, Val F1: 0.8159, Val G-mean: 0.8934\n",
      "Epoch: 559, Val AUC: 0.9316, Val AP: 0.8522, Val F1: 0.8007, Val G-mean: 0.8885\n",
      "Epoch: 569, Val AUC: 0.9350, Val AP: 0.8554, Val F1: 0.8028, Val G-mean: 0.8840\n",
      "Epoch: 579, Val AUC: 0.9543, Val AP: 0.8714, Val F1: 0.8147, Val G-mean: 0.8956\n",
      "Epoch: 589, Val AUC: 0.9505, Val AP: 0.8757, Val F1: 0.8254, Val G-mean: 0.9016\n",
      "Epoch: 599, Val AUC: 0.9524, Val AP: 0.8806, Val F1: 0.8204, Val G-mean: 0.9000\n",
      "Epoch: 609, Val AUC: 0.9547, Val AP: 0.8738, Val F1: 0.8655, Val G-mean: 0.9019\n",
      "Epoch: 619, Val AUC: 0.9454, Val AP: 0.8678, Val F1: 0.8858, Val G-mean: 0.8953\n",
      "Epoch: 629, Val AUC: 0.9451, Val AP: 0.8653, Val F1: 0.8672, Val G-mean: 0.8939\n",
      "Epoch: 639, Val AUC: 0.9477, Val AP: 0.8536, Val F1: 0.7891, Val G-mean: 0.8794\n",
      "Epoch: 649, Val AUC: 0.9329, Val AP: 0.8566, Val F1: 0.7951, Val G-mean: 0.8891\n",
      "Epoch: 659, Val AUC: 0.9360, Val AP: 0.8582, Val F1: 0.7904, Val G-mean: 0.8925\n",
      "Epoch: 669, Val AUC: 0.9357, Val AP: 0.8614, Val F1: 0.7363, Val G-mean: 0.8776\n",
      "Epoch: 679, Val AUC: 0.9369, Val AP: 0.8615, Val F1: 0.8428, Val G-mean: 0.9014\n",
      "Epoch: 689, Val AUC: 0.9371, Val AP: 0.8603, Val F1: 0.8629, Val G-mean: 0.8900\n",
      "Epoch: 699, Val AUC: 0.9438, Val AP: 0.8498, Val F1: 0.8074, Val G-mean: 0.8881\n",
      "Epoch: 709, Val AUC: 0.9619, Val AP: 0.8866, Val F1: 0.8275, Val G-mean: 0.9101\n",
      "Epoch: 719, Val AUC: 0.9518, Val AP: 0.8753, Val F1: 0.8679, Val G-mean: 0.8969\n",
      "Epoch: 729, Val AUC: 0.9547, Val AP: 0.8644, Val F1: 0.8446, Val G-mean: 0.9046\n",
      "Epoch: 739, Val AUC: 0.9561, Val AP: 0.8568, Val F1: 0.7725, Val G-mean: 0.9003\n",
      "Epoch: 749, Val AUC: 0.9339, Val AP: 0.8572, Val F1: 0.7768, Val G-mean: 0.8850\n",
      "Epoch: 759, Val AUC: 0.9375, Val AP: 0.8526, Val F1: 0.8017, Val G-mean: 0.8862\n",
      "Epoch: 769, Val AUC: 0.9408, Val AP: 0.8698, Val F1: 0.8804, Val G-mean: 0.9055\n",
      "Epoch: 779, Val AUC: 0.9415, Val AP: 0.8666, Val F1: 0.8662, Val G-mean: 0.9048\n",
      "Epoch: 789, Val AUC: 0.9430, Val AP: 0.8697, Val F1: 0.8036, Val G-mean: 0.9022\n",
      "Epoch: 799, Val AUC: 0.9375, Val AP: 0.8610, Val F1: 0.8522, Val G-mean: 0.9012\n",
      "Epoch: 809, Val AUC: 0.9537, Val AP: 0.8722, Val F1: 0.8540, Val G-mean: 0.9044\n",
      "Epoch: 819, Val AUC: 0.9542, Val AP: 0.8638, Val F1: 0.8424, Val G-mean: 0.9040\n",
      "Epoch: 829, Val AUC: 0.9445, Val AP: 0.8559, Val F1: 0.8881, Val G-mean: 0.8871\n",
      "Epoch: 839, Val AUC: 0.9441, Val AP: 0.8588, Val F1: 0.8654, Val G-mean: 0.8963\n",
      "Epoch: 849, Val AUC: 0.9387, Val AP: 0.8299, Val F1: 0.8286, Val G-mean: 0.8919\n",
      "Epoch: 859, Val AUC: 0.9407, Val AP: 0.8468, Val F1: 0.8118, Val G-mean: 0.8947\n",
      "Epoch: 869, Val AUC: 0.9397, Val AP: 0.8551, Val F1: 0.8825, Val G-mean: 0.8974\n",
      "Epoch: 879, Val AUC: 0.9384, Val AP: 0.8606, Val F1: 0.8526, Val G-mean: 0.8986\n",
      "Epoch: 889, Val AUC: 0.9343, Val AP: 0.8589, Val F1: 0.8407, Val G-mean: 0.9008\n",
      "Epoch: 899, Val AUC: 0.9431, Val AP: 0.8627, Val F1: 0.7912, Val G-mean: 0.8953\n",
      "Epoch: 909, Val AUC: 0.9347, Val AP: 0.8582, Val F1: 0.8660, Val G-mean: 0.8936\n",
      "Epoch: 919, Val AUC: 0.9420, Val AP: 0.8584, Val F1: 0.8579, Val G-mean: 0.9027\n",
      "Epoch: 929, Val AUC: 0.9328, Val AP: 0.8530, Val F1: 0.8685, Val G-mean: 0.8942\n",
      "Epoch: 939, Val AUC: 0.9310, Val AP: 0.8526, Val F1: 0.8074, Val G-mean: 0.8881\n",
      "Epoch: 949, Val AUC: 0.9292, Val AP: 0.8529, Val F1: 0.8289, Val G-mean: 0.8893\n",
      "Epoch: 959, Val AUC: 0.9328, Val AP: 0.8558, Val F1: 0.7326, Val G-mean: 0.8713\n",
      "Epoch: 969, Val AUC: 0.9269, Val AP: 0.8459, Val F1: 0.8571, Val G-mean: 0.8942\n",
      "Epoch: 979, Val AUC: 0.9317, Val AP: 0.8465, Val F1: 0.8084, Val G-mean: 0.8884\n",
      "Epoch: 989, Val AUC: 0.9279, Val AP: 0.8487, Val F1: 0.8327, Val G-mean: 0.8850\n",
      "Epoch: 999, Val AUC: 0.9552, Val AP: 0.8706, Val F1: 0.8522, Val G-mean: 0.9012\n",
      "Test AUC: 0.9575, Test AP: 0.8581, Test F1: 0.8648, Test G-mean: 0.9046\n",
      "generating embedding visualization...\n",
      "(5000, 2)\n",
      "Figure(800x800)\n"
     ]
    }
   ],
   "source": [
    "!{python_executable} /data/run01/sczc619/LML/MetaTSNE/main.py --method hogrl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9e645890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prefix: /data/run01/sczc619/LML/../../data/\n",
      "dist_path: /data/run01/sczc619/LML/../../data/amazon_shortest_distance.pkl\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "prefix = os.path.join(os.path.dirname(os.getcwd()), \"..\", \"..\", \"data/\")\n",
    "\n",
    "print(\"prefix:\", prefix)\n",
    "\n",
    "dist_path = os.path.join(prefix, f\"{args['dataset']}_shortest_distance.pkl\")\n",
    "\n",
    "print(\"dist_path:\", dist_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "247b56b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "loading data...\n",
      "/data/run01/sczc619/LML/MetaTSNE/data/\n",
      "training...\n",
      "Epoch: 9, Val AUC: 0.7721, Val AP: 0.2316, Val F1: 0.4492, Val G-mean: 0.6521\n",
      "Epoch: 19, Val AUC: 0.8220, Val AP: 0.3707, Val F1: 0.6004, Val G-mean: 0.7457\n",
      "Epoch: 29, Val AUC: 0.9103, Val AP: 0.7867, Val F1: 0.5526, Val G-mean: 0.7529\n",
      "Epoch: 39, Val AUC: 0.9313, Val AP: 0.7873, Val F1: 0.3864, Val G-mean: 0.5926\n",
      "Epoch: 49, Val AUC: 0.9486, Val AP: 0.8551, Val F1: 0.7191, Val G-mean: 0.8671\n",
      "Epoch: 59, Val AUC: 0.9105, Val AP: 0.8051, Val F1: 0.8261, Val G-mean: 0.8636\n",
      "Epoch: 69, Val AUC: 0.9433, Val AP: 0.7349, Val F1: 0.4655, Val G-mean: 0.6866\n",
      "Epoch: 79, Val AUC: 0.8999, Val AP: 0.8312, Val F1: 0.5363, Val G-mean: 0.7367\n",
      "Epoch: 89, Val AUC: 0.9486, Val AP: 0.8213, Val F1: 0.5329, Val G-mean: 0.7512\n",
      "Epoch: 99, Val AUC: 0.9380, Val AP: 0.7197, Val F1: 0.5514, Val G-mean: 0.7702\n",
      "Epoch: 109, Val AUC: 0.9547, Val AP: 0.8790, Val F1: 0.7050, Val G-mean: 0.8622\n",
      "Epoch: 119, Val AUC: 0.9533, Val AP: 0.8639, Val F1: 0.7172, Val G-mean: 0.8685\n",
      "Epoch: 129, Val AUC: 0.9355, Val AP: 0.7515, Val F1: 0.6154, Val G-mean: 0.8111\n",
      "Epoch: 139, Val AUC: 0.9605, Val AP: 0.8714, Val F1: 0.9124, Val G-mean: 0.8737\n",
      "Epoch: 149, Val AUC: 0.9567, Val AP: 0.8867, Val F1: 0.8667, Val G-mean: 0.9022\n",
      "Epoch: 159, Val AUC: 0.9548, Val AP: 0.8654, Val F1: 0.9010, Val G-mean: 0.8838\n",
      "Epoch: 169, Val AUC: 0.9543, Val AP: 0.8813, Val F1: 0.8133, Val G-mean: 0.9004\n",
      "Epoch: 179, Val AUC: 0.9139, Val AP: 0.8597, Val F1: 0.8474, Val G-mean: 0.8944\n",
      "Epoch: 189, Val AUC: 0.9588, Val AP: 0.8794, Val F1: 0.8488, Val G-mean: 0.9003\n",
      "Epoch: 199, Val AUC: 0.9541, Val AP: 0.8389, Val F1: 0.8892, Val G-mean: 0.8724\n",
      "Epoch: 209, Val AUC: 0.9561, Val AP: 0.8788, Val F1: 0.9041, Val G-mean: 0.8691\n",
      "Epoch: 219, Val AUC: 0.9478, Val AP: 0.8507, Val F1: 0.6254, Val G-mean: 0.8239\n",
      "Epoch: 229, Val AUC: 0.9412, Val AP: 0.8560, Val F1: 0.6306, Val G-mean: 0.8222\n",
      "Epoch: 239, Val AUC: 0.9571, Val AP: 0.8840, Val F1: 0.8944, Val G-mean: 0.8854\n",
      "Epoch: 249, Val AUC: 0.9500, Val AP: 0.8061, Val F1: 0.7407, Val G-mean: 0.8819\n",
      "Epoch: 259, Val AUC: 0.9130, Val AP: 0.8511, Val F1: 0.7259, Val G-mean: 0.8613\n",
      "Epoch: 269, Val AUC: 0.9548, Val AP: 0.8388, Val F1: 0.5156, Val G-mean: 0.7369\n",
      "Epoch: 279, Val AUC: 0.8950, Val AP: 0.4691, Val F1: 0.5904, Val G-mean: 0.7899\n",
      "Epoch: 289, Val AUC: 0.9551, Val AP: 0.8853, Val F1: 0.9025, Val G-mean: 0.8436\n",
      "Epoch: 299, Val AUC: 0.9434, Val AP: 0.8453, Val F1: 0.6353, Val G-mean: 0.8311\n",
      "Epoch: 309, Val AUC: 0.9614, Val AP: 0.8636, Val F1: 0.4892, Val G-mean: 0.7135\n",
      "Epoch: 319, Val AUC: 0.9575, Val AP: 0.8670, Val F1: 0.6202, Val G-mean: 0.8253\n",
      "Epoch: 329, Val AUC: 0.9593, Val AP: 0.8866, Val F1: 0.8627, Val G-mean: 0.9095\n",
      "Epoch: 339, Val AUC: 0.9611, Val AP: 0.8803, Val F1: 0.8211, Val G-mean: 0.9029\n",
      "Epoch: 349, Val AUC: 0.9561, Val AP: 0.8798, Val F1: 0.8655, Val G-mean: 0.9019\n",
      "Epoch: 359, Val AUC: 0.9527, Val AP: 0.8694, Val F1: 0.7192, Val G-mean: 0.8694\n",
      "Epoch: 369, Val AUC: 0.9362, Val AP: 0.8625, Val F1: 0.9019, Val G-mean: 0.8809\n",
      "Epoch: 379, Val AUC: 0.9393, Val AP: 0.7394, Val F1: 0.5763, Val G-mean: 0.7864\n",
      "Epoch: 389, Val AUC: 0.9574, Val AP: 0.8721, Val F1: 0.7861, Val G-mean: 0.8885\n",
      "Epoch: 399, Val AUC: 0.9612, Val AP: 0.8780, Val F1: 0.8574, Val G-mean: 0.8858\n",
      "Epoch: 409, Val AUC: 0.9471, Val AP: 0.8520, Val F1: 0.8830, Val G-mean: 0.8620\n",
      "Epoch: 419, Val AUC: 0.9537, Val AP: 0.8224, Val F1: 0.5598, Val G-mean: 0.7794\n",
      "Epoch: 429, Val AUC: 0.9294, Val AP: 0.8403, Val F1: 0.8632, Val G-mean: 0.8757\n",
      "Epoch: 439, Val AUC: 0.9272, Val AP: 0.6546, Val F1: 0.4027, Val G-mean: 0.6138\n",
      "Epoch: 449, Val AUC: 0.9606, Val AP: 0.8787, Val F1: 0.7920, Val G-mean: 0.8981\n",
      "Epoch: 459, Val AUC: 0.9559, Val AP: 0.8691, Val F1: 0.7210, Val G-mean: 0.8769\n",
      "Epoch: 469, Val AUC: 0.9316, Val AP: 0.8420, Val F1: 0.7996, Val G-mean: 0.8724\n",
      "Epoch: 479, Val AUC: 0.9367, Val AP: 0.8067, Val F1: 0.7583, Val G-mean: 0.8802\n",
      "Epoch: 489, Val AUC: 0.9554, Val AP: 0.8723, Val F1: 0.7256, Val G-mean: 0.8793\n",
      "Epoch: 499, Val AUC: 0.9492, Val AP: 0.8558, Val F1: 0.8504, Val G-mean: 0.8756\n",
      "Epoch: 509, Val AUC: 0.9432, Val AP: 0.8424, Val F1: 0.6123, Val G-mean: 0.8106\n",
      "Epoch: 519, Val AUC: 0.9390, Val AP: 0.8236, Val F1: 0.8426, Val G-mean: 0.8876\n",
      "Epoch: 529, Val AUC: 0.9487, Val AP: 0.7905, Val F1: 0.4762, Val G-mean: 0.6971\n",
      "Epoch: 539, Val AUC: 0.9289, Val AP: 0.6793, Val F1: 0.6402, Val G-mean: 0.8327\n",
      "Epoch: 549, Val AUC: 0.9472, Val AP: 0.8684, Val F1: 0.7164, Val G-mean: 0.8658\n",
      "Epoch: 559, Val AUC: 0.9335, Val AP: 0.6890, Val F1: 0.5287, Val G-mean: 0.7471\n",
      "Epoch: 569, Val AUC: 0.9464, Val AP: 0.8670, Val F1: 0.9009, Val G-mean: 0.8433\n",
      "Epoch: 579, Val AUC: 0.9494, Val AP: 0.8670, Val F1: 0.8024, Val G-mean: 0.8916\n",
      "Epoch: 589, Val AUC: 0.9473, Val AP: 0.8737, Val F1: 0.8953, Val G-mean: 0.8974\n",
      "Epoch: 599, Val AUC: 0.9435, Val AP: 0.8117, Val F1: 0.7686, Val G-mean: 0.8868\n",
      "Epoch: 609, Val AUC: 0.9500, Val AP: 0.8710, Val F1: 0.7385, Val G-mean: 0.8786\n",
      "Epoch: 619, Val AUC: 0.9470, Val AP: 0.8704, Val F1: 0.7915, Val G-mean: 0.8879\n",
      "Epoch: 629, Val AUC: 0.9444, Val AP: 0.8658, Val F1: 0.9124, Val G-mean: 0.8737\n",
      "Epoch: 639, Val AUC: 0.9487, Val AP: 0.8609, Val F1: 0.9058, Val G-mean: 0.8786\n",
      "Epoch: 649, Val AUC: 0.9507, Val AP: 0.8692, Val F1: 0.7977, Val G-mean: 0.8926\n",
      "Epoch: 659, Val AUC: 0.9354, Val AP: 0.7780, Val F1: 0.5823, Val G-mean: 0.7899\n",
      "Epoch: 669, Val AUC: 0.9443, Val AP: 0.8545, Val F1: 0.8378, Val G-mean: 0.8891\n",
      "Epoch: 679, Val AUC: 0.9428, Val AP: 0.8518, Val F1: 0.6675, Val G-mean: 0.8489\n",
      "Epoch: 689, Val AUC: 0.9425, Val AP: 0.8593, Val F1: 0.7768, Val G-mean: 0.8850\n",
      "Epoch: 699, Val AUC: 0.9468, Val AP: 0.8742, Val F1: 0.7967, Val G-mean: 0.8923\n",
      "Epoch: 709, Val AUC: 0.9492, Val AP: 0.8730, Val F1: 0.9032, Val G-mean: 0.8720\n",
      "Epoch: 719, Val AUC: 0.9448, Val AP: 0.8112, Val F1: 0.8152, Val G-mean: 0.8905\n",
      "Epoch: 729, Val AUC: 0.9451, Val AP: 0.8636, Val F1: 0.8841, Val G-mean: 0.8803\n",
      "Epoch: 739, Val AUC: 0.9393, Val AP: 0.8530, Val F1: 0.9017, Val G-mean: 0.8717\n",
      "Epoch: 749, Val AUC: 0.9542, Val AP: 0.8776, Val F1: 0.8164, Val G-mean: 0.8883\n",
      "Epoch: 759, Val AUC: 0.9444, Val AP: 0.8556, Val F1: 0.8659, Val G-mean: 0.8879\n",
      "Epoch: 769, Val AUC: 0.9340, Val AP: 0.8558, Val F1: 0.8013, Val G-mean: 0.8756\n",
      "Epoch: 779, Val AUC: 0.9464, Val AP: 0.8520, Val F1: 0.8495, Val G-mean: 0.8810\n",
      "Epoch: 789, Val AUC: 0.9507, Val AP: 0.8603, Val F1: 0.6889, Val G-mean: 0.8638\n",
      "Epoch: 799, Val AUC: 0.9462, Val AP: 0.8679, Val F1: 0.9060, Val G-mean: 0.8877\n",
      "Epoch: 809, Val AUC: 0.9374, Val AP: 0.8542, Val F1: 0.8333, Val G-mean: 0.8796\n",
      "Epoch: 819, Val AUC: 0.9464, Val AP: 0.8482, Val F1: 0.8821, Val G-mean: 0.8769\n",
      "Epoch: 829, Val AUC: 0.9428, Val AP: 0.8528, Val F1: 0.8926, Val G-mean: 0.8761\n",
      "Epoch: 839, Val AUC: 0.9543, Val AP: 0.8781, Val F1: 0.8274, Val G-mean: 0.9022\n",
      "Epoch: 849, Val AUC: 0.9556, Val AP: 0.8864, Val F1: 0.6836, Val G-mean: 0.8667\n",
      "Epoch: 859, Val AUC: 0.9345, Val AP: 0.7635, Val F1: 0.6435, Val G-mean: 0.8331\n",
      "Epoch: 869, Val AUC: 0.9402, Val AP: 0.8007, Val F1: 0.7820, Val G-mean: 0.8794\n",
      "Epoch: 879, Val AUC: 0.9489, Val AP: 0.8754, Val F1: 0.8667, Val G-mean: 0.9022\n",
      "Epoch: 889, Val AUC: 0.9323, Val AP: 0.8516, Val F1: 0.7974, Val G-mean: 0.8770\n",
      "Epoch: 899, Val AUC: 0.9343, Val AP: 0.7688, Val F1: 0.7374, Val G-mean: 0.8712\n",
      "Epoch: 909, Val AUC: 0.9475, Val AP: 0.8718, Val F1: 0.8914, Val G-mean: 0.8907\n",
      "Epoch: 919, Val AUC: 0.9468, Val AP: 0.8651, Val F1: 0.7262, Val G-mean: 0.8661\n",
      "Epoch: 929, Val AUC: 0.9327, Val AP: 0.7029, Val F1: 0.6010, Val G-mean: 0.8086\n",
      "Epoch: 939, Val AUC: 0.9461, Val AP: 0.8648, Val F1: 0.7899, Val G-mean: 0.8822\n",
      "Epoch: 949, Val AUC: 0.9440, Val AP: 0.8606, Val F1: 0.9031, Val G-mean: 0.8469\n",
      "Epoch: 959, Val AUC: 0.9445, Val AP: 0.8617, Val F1: 0.7881, Val G-mean: 0.8816\n",
      "Epoch: 969, Val AUC: 0.9449, Val AP: 0.8651, Val F1: 0.8455, Val G-mean: 0.8912\n",
      "Epoch: 979, Val AUC: 0.9510, Val AP: 0.8727, Val F1: 0.8825, Val G-mean: 0.8974\n",
      "Epoch: 989, Val AUC: 0.9430, Val AP: 0.8597, Val F1: 0.8917, Val G-mean: 0.8352\n",
      "Epoch: 999, Val AUC: 0.9153, Val AP: 0.6848, Val F1: 0.7544, Val G-mean: 0.8540\n",
      "Test AUC: 0.9349, Test AP: 0.7394, Test F1: 0.7743, Test G-mean: 0.8775\n",
      "generating embedding visualization...\n",
      "(5000, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from methods.hogrl_meta.hogrl_meta_main import *\n",
    "from methods.hogrl_meta.hogrl_meta_model import *\n",
    "from methods.hogrl_meta.hogrl_meta_utils import *\n",
    "args = {\n",
    "    \"dataset\": \"amazon\",\n",
    "    \"batch_size\": 256,\n",
    "    \"lr\": 0.005,\n",
    "    \"weight_decay\": 0.00005,\n",
    "    \"emb_size\": 64,\n",
    "    \"num_epochs\": 1000,\n",
    "    \"weight\": 0.6,\n",
    "    \"layers\": 7,\n",
    "    \"test_size\": 0.6,\n",
    "    \"val_size\": 0.5,\n",
    "    \"layers_tree\": 7,\n",
    "    \"seed\": 76,\n",
    "    \"drop_rate\": 0.3,\n",
    "    \"data_prefix\":\"/data/run01/sczc619/LML/MetaTSNE/data/\"\n",
    "}\n",
    "\n",
    "hogrl_meta_main(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "700fbbf5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tsne01",
   "language": "python",
   "name": "tsne01"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
